In [1]:
import matplotlib.pyplot as plt
import plotly.express as px
import pandas as pd
import numpy as np
import seaborn as sns
from datetime import datetime,tzinfo
import pytz
In [2]:
agri_data = pd.read_csv("India Agriculture Crop Production.csv")
agri_data.dropna(inplace = True)
agri_data
Out[2]:
State District Crop Year Season Area Area Units Production Production Units Yield
0 Andaman and Nicobar Islands NICOBARS Arecanut 2001-02 Kharif 1254.0 Hectare 2061.0 Tonnes 1.643541
1 Andaman and Nicobar Islands NICOBARS Arecanut 2002-03 Whole Year 1258.0 Hectare 2083.0 Tonnes 1.655803
2 Andaman and Nicobar Islands NICOBARS Arecanut 2003-04 Whole Year 1261.0 Hectare 1525.0 Tonnes 1.209358
3 Andaman and Nicobar Islands NORTH AND MIDDLE ANDAMAN Arecanut 2001-02 Kharif 3100.0 Hectare 5239.0 Tonnes 1.690000
4 Andaman and Nicobar Islands SOUTH ANDAMANS Arecanut 2002-03 Whole Year 3105.0 Hectare 5267.0 Tonnes 1.696296
... ... ... ... ... ... ... ... ... ... ...
345370 West Bengal PURBA BARDHAMAN Wheat 2000-01 Rabi 6310.0 Hectare 15280.0 Tonnes 2.421553
345371 West Bengal PURULIA Wheat 1997-98 Rabi 1895.0 Hectare 2760.0 Tonnes 1.456464
345372 West Bengal PURULIA Wheat 1998-99 Rabi 3736.0 Hectare 5530.0 Tonnes 1.480193
345373 West Bengal PURULIA Wheat 1999-00 Rabi 2752.0 Hectare 6928.0 Tonnes 2.517442
345374 West Bengal PURULIA Wheat 2000-01 Rabi 2979.0 Hectare 7430.0 Tonnes 2.494126

340414 rows × 10 columns

In [3]:
plt.figure(figsize=(16,6))
sns.lineplot(data=agri_data);

C:\Users\sreya\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\sreya\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [4]:
round(pd.pivot_table(agri_data, index = ["Year"], values=["Yield"], columns=["State"]),2)
Out[4]:
Yield
State Andaman and Nicobar Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh Dadra and Nagar Haveli Daman and Diu Delhi ... Puducherry Punjab Rajasthan Sikkim Tamil Nadu Telangana Tripura Uttar Pradesh Uttarakhand West Bengal
Year
1997-98 NaN 3.86 2.39 215.52 3.95 NaN NaN NaN NaN NaN ... NaN 6.91 4.44 1.08 NaN NaN NaN 3.97 NaN 470.77
1998-99 NaN 4.08 2.65 225.00 3.57 6.13 NaN 23.26 1.25 7.59 ... 9.13 6.87 2.87 0.88 11.33 NaN 3.69 4.18 NaN 438.00
1999-00 NaN 4.08 3.39 210.38 3.97 5.14 NaN 28.76 1.16 6.70 ... 1054.74 7.20 2.47 1.15 9.59 NaN 10.76 4.91 NaN 424.10
2000-01 337.64 197.46 3.61 214.88 3.57 5.75 2.12 30.07 1.60 6.94 ... 908.31 7.90 2.32 0.97 8.91 NaN 11.05 4.06 3.72 454.37
2001-02 449.48 191.77 3.79 249.13 3.91 6.48 2.18 21.13 0.76 7.90 ... 958.40 8.52 2.55 0.96 10.13 NaN 11.21 4.33 3.53 414.49
2002-03 398.34 192.12 3.80 225.14 3.49 9.41 2.04 21.76 0.95 8.10 ... 1313.50 6.72 2.29 1.34 339.57 NaN 8.49 4.70 3.55 387.56
2003-04 397.05 191.36 3.72 225.73 3.43 9.82 2.11 20.25 1.94 8.86 ... 1237.29 6.16 2.85 1.38 303.56 NaN 9.02 4.58 3.36 403.53
2004-05 407.79 217.70 3.71 2.92 3.13 9.20 1.94 18.49 2.09 9.38 ... 1119.33 6.65 2.87 1.46 423.49 NaN 3.86 6.46 3.31 443.93
2005-06 399.85 168.07 3.62 237.46 3.32 9.32 1.95 18.64 1.16 8.26 ... 1192.91 6.77 3.15 2.53 464.64 NaN 3.84 6.86 3.67 399.43
2006-07 342.55 255.00 3.71 202.35 1.58 8.93 2.09 19.77 1.61 10.00 ... 13.22 2.03 3.33 1.48 529.50 NaN 3.90 5.84 3.19 448.23
2007-08 159.79 5.30 3.74 220.72 3.43 9.23 2.08 1.10 1.41 12.53 ... 735.91 2.27 2.98 1.22 517.47 NaN 3.86 4.92 3.24 366.35
2008-09 86.35 200.22 3.76 244.20 3.59 9.82 1.88 1.10 1.35 12.03 ... 756.68 2.26 3.10 1.07 598.24 NaN 4.23 4.99 3.04 373.70
2009-10 82.01 214.18 3.97 265.19 3.15 9.89 2.74 0.93 1.00 40.11 ... 731.43 2.64 3.41 1.18 564.44 NaN 4.00 5.88 2.96 438.89
2010-11 228.23 247.54 4.09 226.07 3.90 10.11 1.98 5.28 1.26 47.83 ... 753.05 2.35 3.66 1.58 9.76 NaN 4.07 5.13 3.15 431.82
2011-12 268.82 269.87 4.12 233.07 1.96 9.18 2.29 5.41 1.10 47.44 ... 683.97 8.98 3.76 1.08 450.19 NaN 3.98 5.86 3.62 445.47
2012-13 309.72 357.90 4.19 198.38 4.10 11.73 1.58 5.46 1.25 14.92 ... 792.92 8.46 3.89 1.11 8.95 NaN 4.14 6.19 3.45 449.91
2013-14 294.39 316.47 4.33 207.41 4.05 11.41 2.46 1.32 1.57 14.92 ... 551.77 9.49 3.89 1.13 283.03 101.68 4.26 6.01 3.22 455.54
2014-15 296.59 278.99 4.46 216.43 4.47 10.64 2.41 1.33 1.84 7.32 ... 803.80 8.81 3.82 1.13 309.27 126.74 4.34 5.94 3.52 463.63
2015-16 319.56 269.59 4.58 246.30 4.46 9.45 1.85 6.97 1.24 2.94 ... 913.75 8.78 4.22 1.14 341.12 5.68 3.87 5.92 3.18 454.87
2016-17 344.99 288.36 2.58 274.18 4.95 4.97 1.87 5.79 1.31 2.92 ... 738.05 8.94 4.14 1.14 321.27 5.73 3.51 6.44 3.35 401.23
2017-18 617.88 305.36 2.58 286.26 4.88 5.10 1.64 6.79 1.28 2.58 ... 756.58 8.84 4.98 1.15 246.41 7.62 3.41 6.98 3.98 453.92
2018-19 392.01 371.38 2.69 272.64 5.14 5.15 1.77 7.36 1.29 2.91 ... 537.85 8.12 4.15 1.15 298.44 7.12 3.43 7.20 4.00 462.30
2019-20 376.00 385.10 2.71 279.95 5.03 4.33 2.18 NaN NaN 3.03 ... 639.61 9.22 3.93 1.16 253.91 7.20 3.38 7.29 4.13 460.62
2020-21 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN 4.14 NaN

24 rows × 36 columns

In [5]:
fig=px.line(agri_data,x="Year",y="Yield",color="State",title="Indian Agriculture States",markers=True)
fig.show()
In [6]:
df= agri_data[["Year","State",'District',"Season","Crop",'Yield']].sort_values("Yield",ascending = False).round(2)
df
Out[6]:
Year State District Season Crop Yield
228210 2018-19 Assam TINSUKIA Whole Year Coconut 43958.33
83550 2006-07 West Bengal BIRBHUM Whole Year Coconut 38800.00
119409 2008-09 Tamil Nadu ERODE Whole Year Coconut 33133.61
279641 2019-20 Assam TINSUKIA Whole Year Coconut 32957.75
225028 2018-19 Andhra Pradesh PRAKASAM Whole Year Coconut 31578.95
... ... ... ... ... ... ...
323808 1999-00 Maharashtra NASHIK Rabi Sesamum 0.00
12267 2002-03 Haryana FARIDABAD Whole Year Banana 0.00
12268 2003-04 Haryana FARIDABAD Whole Year Banana 0.00
12269 2002-03 Haryana JHAJJAR Whole Year Banana 0.00
72026 2004-05 Punjab GURDASPUR Kharif Moth 0.00

340414 rows × 6 columns

In [7]:
df.describe().round(2)
Out[7]:
Yield
count 340414.00
mean 80.56
std 923.23
min 0.00
25% 0.57
50% 1.02
75% 2.50
max 43958.33
In [8]:
px.treemap(df,values="Yield",color="State",path=["Season","State","District"],height=1000)
In [9]:
px.sunburst(df,values="Yield",color="State",path=["Crop","State"])
In [10]:
px.bar(df.head(10), x="Year",y="Yield", hover_data=["State","District","Season","Crop"],title="Top 10 Yields",color="District")
In [11]:
df1 = agri_data[["Year","State",'District',"Season","Crop",'Yield']].sort_values("Year",ascending = False).round(2)
df1
Out[11]:
Year State District Season Crop Yield
297030 2020-21 Uttarakhand UTTAR KASHI Kharif other oilseeds 0.75
296901 2020-21 Uttarakhand UTTAR KASHI Kharif Maize 2.24
297190 2020-21 Uttarakhand CHAMPAWAT Kharif Sesamum 0.21
296903 2020-21 Uttarakhand ALMORA Rabi Masoor 0.68
296755 2020-21 Uttarakhand PAURI GARHWAL Rabi Barley 1.28
... ... ... ... ... ... ...
328501 1997-98 Odisha KENDUJHAR Winter Sesamum 0.20
302433 1997-98 Assam NALBARI Rabi Other Rabi pulses 0.64
328508 1997-98 Odisha KHORDHA Summer Sesamum 0.31
328509 1997-98 Odisha KHORDHA Winter Sesamum 0.09
315037 1997-98 Karnataka TUMKUR Kharif Jowar 0.59

340414 rows × 6 columns

In [12]:
fig=px.scatter(df1.head(10),y = "Yield",x = "Year", hover_data = ["State","District","Season","Crop"],title = "Top 10 Crops (2020-21)",
               color ="District",facet_col = "Crop",facet_row = "Season",symbol="Crop",size="Yield")
fig.show()
In [13]:
fig=px.scatter(df1.head(10),y = "Yield",x = "Year", hover_data = ["State","District","Season","Crop"],title = "Top 10 Crops (2020-21)",
               color ="District",marginal_x = "histogram",marginal_y = "rug",symbol="Season",size="Yield")
fig.show()
In [14]:
data_WB = agri_data.query("State=='West Bengal'")
data_WB
Out[14]:
State District Crop Year Season Area Area Units Production Production Units Yield
40471 West Bengal 24 PARAGANAS NORTH Arecanut 2001-02 Whole Year 1452.0 Hectare 2680.0 Tonnes 1.845730
40472 West Bengal 24 PARAGANAS NORTH Arecanut 2002-03 Whole Year 1486.0 Hectare 3856.0 Tonnes 2.594886
40473 West Bengal 24 PARAGANAS NORTH Arecanut 2003-04 Whole Year 1540.0 Hectare 4457.0 Tonnes 2.894156
40474 West Bengal 24 PARAGANAS SOUTH Arecanut 2001-02 Whole Year 875.0 Hectare 1575.0 Tonnes 1.800000
40475 West Bengal 24 PARAGANAS SOUTH Arecanut 2002-03 Whole Year 889.0 Hectare 1645.0 Tonnes 1.850394
... ... ... ... ... ... ... ... ... ... ...
345370 West Bengal PURBA BARDHAMAN Wheat 2000-01 Rabi 6310.0 Hectare 15280.0 Tonnes 2.421553
345371 West Bengal PURULIA Wheat 1997-98 Rabi 1895.0 Hectare 2760.0 Tonnes 1.456464
345372 West Bengal PURULIA Wheat 1998-99 Rabi 3736.0 Hectare 5530.0 Tonnes 1.480193
345373 West Bengal PURULIA Wheat 1999-00 Rabi 2752.0 Hectare 6928.0 Tonnes 2.517442
345374 West Bengal PURULIA Wheat 2000-01 Rabi 2979.0 Hectare 7430.0 Tonnes 2.494126

12580 rows × 10 columns

In [15]:
df2 = data_WB[["Year",'District',"Season","Crop",'Yield']].sort_values("Yield",ascending = False).round(2)
df2
Out[15]:
Year District Season Crop Yield
83550 2006-07 BIRBHUM Whole Year Coconut 38800.00
83541 2006-07 24 PARAGANAS NORTH Whole Year Coconut 22497.00
40598 2001-02 24 PARAGANAS NORTH Whole Year Coconut 21294.00
343440 1997-98 DINAJPUR UTTAR Whole Year Coconut 20975.18
83539 2004-05 24 PARAGANAS NORTH Whole Year Coconut 20899.00
... ... ... ... ... ...
344221 1997-98 COOCHBEHAR Summer Moong(Green Gram) 0.02
344220 1997-98 COOCHBEHAR Rabi Moong(Green Gram) 0.02
276958 2018-19 PURBA BARDHAMAN Kharif Jute 0.02
345094 1997-98 24 PARAGANAS SOUTH Whole Year Sunflower 0.00
343405 1997-98 DARJEELING Whole Year Cardamom 0.00

12580 rows × 5 columns

In [16]:
px.treemap(df2,values="Yield",color="District",path=["Crop","District"])
In [17]:
px.bar(df2.head(10), x="Year",y="Yield", hover_data=["District","Season","Crop"],title="Top 10 Yields of WB",color="District")
In [18]:
df3 = data_WB[["Year",'District',"Season","Crop",'Yield']].sort_values("Year",ascending = False).round(2)
df3
Out[18]:
Year District Season Crop Yield
297386 2019-20 MALDAH Whole Year Coconut 13403.88
297677 2019-20 NADIA Rabi Peas & beans (Pulses) 0.81
297684 2019-20 BIRBHUM Rabi Potato 28.19
297407 2019-20 MEDINIPUR WEST Rabi Gram 1.60
297683 2019-20 BANKURA Rabi Potato 17.09
... ... ... ... ... ...
343436 1997-98 DINAJPUR DAKSHIN Whole Year Coconut 18234.69
344574 1997-98 HOOGHLY Whole Year Rapeseed &Mustard 0.76
344578 1997-98 HOWRAH Whole Year Rapeseed &Mustard 0.58
344582 1997-98 JALPAIGURI Whole Year Rapeseed &Mustard 0.44
344522 1997-98 COOCHBEHAR Autumn Ragi 0.40

12580 rows × 5 columns

In [19]:
fig=px.line(df3,x="Year",y="Yield",color="Crop",title="WB Agriculture")
fig.show()
In [20]:
fig=px.scatter(df3,y = "Yield",x = "Year", hover_data = ["District","Season","Crop"],title = "Crops in WB",
               color ="District",symbol="Season",size="Yield")
fig.show()
In [21]:
df4=round(pd.pivot_table(data_WB, index = ["Year"], values=["Yield"], columns=["District"]),2)
df4
Out[21]:
Yield
District 24 PARAGANAS NORTH 24 PARAGANAS SOUTH ALIPURDUAR BANKURA BIRBHUM COOCHBEHAR DARJEELING DINAJPUR DAKSHIN DINAJPUR UTTAR HOOGHLY ... JHARGRAM KALIMPONG MALDAH MEDINIPUR EAST MEDINIPUR WEST MURSHIDABAD NADIA PASCHIM BARDHAMAN PURBA BARDHAMAN PURULIA
Year
1997-98 524.38 504.86 NaN 172.30 155.30 446.67 193.33 680.29 727.99 873.79 ... NaN NaN 204.91 536.50 566.82 225.93 625.51 NaN 414.56 610.20
1998-99 721.50 448.91 NaN 176.99 155.04 402.22 193.10 582.10 437.99 873.85 ... NaN NaN 201.14 501.37 496.17 190.42 560.59 NaN 423.49 488.39
1999-00 618.79 448.82 NaN 256.98 144.09 357.32 587.04 599.50 446.62 772.38 ... NaN NaN 179.37 469.07 442.51 247.64 445.10 NaN 407.31 406.85
2000-01 838.03 442.33 NaN 304.54 230.21 66.72 676.87 661.59 513.18 776.27 ... NaN NaN 191.33 511.16 476.97 281.97 459.97 NaN 409.33 439.05
2001-02 794.14 545.32 NaN 316.39 209.84 71.80 580.97 703.07 340.03 777.29 ... NaN NaN 8.13 666.34 529.80 230.69 446.91 NaN 421.79 346.94
2002-03 690.96 622.88 NaN 265.26 246.42 62.64 474.93 619.39 246.03 910.71 ... NaN NaN 179.51 468.32 414.62 258.92 415.36 NaN 477.01 188.36
2003-04 779.97 569.85 NaN 370.22 294.61 65.01 519.63 672.13 244.54 760.49 ... NaN NaN 191.49 600.91 464.93 216.22 437.84 NaN 538.18 95.00
2004-05 842.15 571.40 NaN 421.63 297.56 417.61 570.72 739.45 215.61 647.39 ... NaN NaN 191.61 604.57 489.52 253.28 464.76 NaN 561.60 134.57
2005-06 675.60 484.49 NaN 297.46 165.73 415.32 536.27 737.51 168.56 627.69 ... NaN NaN 188.52 500.66 467.50 386.21 394.40 NaN 473.14 116.51
2006-07 871.00 539.72 NaN 317.02 1255.78 424.96 537.64 57.05 170.85 563.85 ... NaN NaN 192.67 537.47 440.81 475.38 435.47 NaN 400.61 142.24
2007-08 453.98 491.39 NaN 273.00 224.62 241.81 415.35 514.05 268.36 487.71 ... NaN NaN 379.35 560.48 369.13 344.19 339.97 NaN 316.64 362.93
2008-09 484.95 528.06 NaN 310.31 243.09 369.67 414.76 562.44 243.68 467.66 ... NaN NaN 399.95 521.54 350.56 343.14 367.02 NaN 333.69 254.19
2009-10 522.90 621.80 NaN 369.47 285.43 428.09 476.26 695.23 329.59 578.74 ... NaN NaN 438.07 671.87 439.66 423.14 399.53 NaN 366.15 283.41
2010-11 524.72 572.70 NaN 368.56 295.06 427.94 464.39 694.42 285.35 551.04 ... NaN NaN 451.85 709.59 401.38 436.97 377.31 NaN 378.39 308.09
2011-12 546.59 658.66 NaN 384.93 294.84 433.84 436.99 807.76 310.68 576.11 ... NaN NaN 472.62 669.56 404.60 436.28 393.70 NaN 376.38 326.76
2012-13 548.70 630.28 NaN 437.27 276.35 446.02 440.52 779.46 365.71 552.30 ... NaN NaN 472.07 632.49 406.51 438.14 405.91 NaN 354.64 346.10
2013-14 551.03 629.74 NaN 436.67 292.77 417.35 526.87 729.59 398.13 596.32 ... NaN NaN 461.01 633.00 409.52 423.11 429.93 NaN 353.86 312.16
2014-15 508.90 629.11 14610.43 420.33 283.62 418.12 449.73 729.31 361.25 546.07 ... NaN NaN 446.95 632.88 386.84 423.45 431.64 NaN 322.69 295.15
2015-16 507.30 630.33 14639.64 410.81 273.43 405.39 436.96 649.92 340.03 541.92 ... NaN NaN 435.55 632.44 383.52 409.94 412.71 NaN 342.74 304.09
2016-17 470.16 629.37 480.65 390.77 266.39 408.56 418.52 606.71 339.91 499.53 ... 7.51 2.68 429.40 633.18 415.83 410.23 416.01 10.25 354.02 309.06
2017-18 481.44 621.96 456.73 336.67 275.28 418.00 331.79 556.96 366.88 555.11 ... 565.55 802.42 531.94 671.01 480.21 408.02 433.47 434.09 430.99 317.82
2018-19 480.62 601.96 417.62 358.62 293.51 430.95 397.11 544.06 407.38 530.09 ... 446.74 844.63 576.46 548.62 547.98 533.13 442.81 415.59 415.49 331.62
2019-20 478.93 592.13 425.88 339.80 268.82 414.28 476.18 538.45 427.65 498.99 ... 394.19 906.91 566.38 642.75 499.60 477.02 431.74 475.60 428.62 328.96

23 rows × 22 columns

In [22]:
df4.shape
Out[22]:
(23, 22)
In [23]:
df4.index
Out[23]:
Index(['1997-98', '1998-99', '1999-00', '2000-01', '2001-02', '2002-03',
       '2003-04', '2004-05', '2005-06', '2006-07', '2007-08', '2008-09',
       '2009-10', '2010-11', '2011-12', '2012-13', '2013-14', '2014-15',
       '2015-16', '2016-17', '2017-18', '2018-19', '2019-20'],
      dtype='object', name='Year')
In [24]:
df4.columns
Out[24]:
MultiIndex([('Yield', '24 PARAGANAS NORTH'),
            ('Yield', '24 PARAGANAS SOUTH'),
            ('Yield',         'ALIPURDUAR'),
            ('Yield',            'BANKURA'),
            ('Yield',            'BIRBHUM'),
            ('Yield',         'COOCHBEHAR'),
            ('Yield',         'DARJEELING'),
            ('Yield',   'DINAJPUR DAKSHIN'),
            ('Yield',     'DINAJPUR UTTAR'),
            ('Yield',            'HOOGHLY'),
            ('Yield',             'HOWRAH'),
            ('Yield',         'JALPAIGURI'),
            ('Yield',           'JHARGRAM'),
            ('Yield',          'KALIMPONG'),
            ('Yield',             'MALDAH'),
            ('Yield',     'MEDINIPUR EAST'),
            ('Yield',     'MEDINIPUR WEST'),
            ('Yield',        'MURSHIDABAD'),
            ('Yield',              'NADIA'),
            ('Yield',  'PASCHIM BARDHAMAN'),
            ('Yield',    'PURBA BARDHAMAN'),
            ('Yield',            'PURULIA')],
           names=[None, 'District'])
In [25]:
type(df4)
Out[25]:
pandas.core.frame.DataFrame
In [26]:
len(df4)
Out[26]:
23
In [27]:
max(df4.index)
Out[27]:
'2019-20'
In [28]:
min(df4)
Out[28]:
('Yield', '24 PARAGANAS NORTH')
In [29]:
df4.describe().round(2)
Out[29]:
Yield
District 24 PARAGANAS NORTH 24 PARAGANAS SOUTH ALIPURDUAR BANKURA BIRBHUM COOCHBEHAR DARJEELING DINAJPUR DAKSHIN DINAJPUR UTTAR HOOGHLY ... JHARGRAM KALIMPONG MALDAH MEDINIPUR EAST MEDINIPUR WEST MURSHIDABAD NADIA PASCHIM BARDHAMAN PURBA BARDHAMAN PURULIA
count 23.00 23.00 6.00 23.00 23.00 23.00 23.00 23.00 23.00 23.00 ... 4.00 4.00 23.00 23.00 23.00 23.00 23.00 4.00 23.00 23.00
mean 605.08 565.92 5171.82 336.35 292.51 347.40 458.95 628.71 345.91 633.27 ... 353.50 639.16 338.71 589.38 447.17 359.71 433.38 333.88 404.41 306.45
std 138.80 67.99 7322.47 74.12 215.95 138.06 112.96 148.92 122.09 136.95 ... 241.54 426.49 159.48 72.73 57.69 99.96 59.38 217.21 63.43 119.81
min 453.98 442.33 417.62 172.30 144.09 62.64 193.10 57.05 168.56 467.66 ... 7.51 2.68 8.13 468.32 350.56 190.42 339.97 10.25 316.64 95.00
25% 496.12 516.46 433.59 301.00 227.42 363.50 416.94 572.27 257.20 544.00 ... 297.52 602.48 191.55 529.02 405.56 256.10 402.72 314.26 354.33 268.80
50% 546.59 572.70 468.69 339.80 273.43 415.32 464.39 661.59 340.03 576.11 ... 420.46 823.52 399.95 604.57 440.81 408.02 431.64 424.84 407.31 312.16
75% 706.23 626.00 11077.98 387.85 293.14 426.45 531.57 716.19 402.76 766.44 ... 476.44 860.20 456.43 637.96 484.86 429.86 443.96 444.47 426.06 346.52
max 871.00 658.66 14639.64 437.27 1255.78 446.67 676.87 807.76 727.99 910.71 ... 565.55 906.91 576.46 709.59 566.82 533.13 625.51 475.60 561.60 610.20

8 rows × 22 columns

In [30]:
fig=px.box(df3,y="District",x="Year")
fig.show()
In [31]:
df5 = df3.query("Year=='2019-20'")
df6=df5.sort_values("Yield",ascending = False)
df6
Out[31]:
Year District Season Crop Yield
297374 2019-20 ALIPURDUAR Whole Year Coconut 14288.54
297388 2019-20 MEDINIPUR WEST Whole Year Coconut 13847.05
297389 2019-20 MURSHIDABAD Whole Year Coconut 13651.41
297383 2019-20 JALPAIGURI Whole Year Coconut 13623.96
297377 2019-20 COOCHBEHAR Whole Year Coconut 13477.72
... ... ... ... ... ...
297512 2019-20 MEDINIPUR WEST Rabi Linseed 0.20
297504 2019-20 BANKURA Rabi Linseed 0.20
297516 2019-20 PURULIA Rabi Linseed 0.17
297627 2019-20 HOWRAH Summer Moong(Green Gram) 0.08
297817 2019-20 HOWRAH Summer Sesamum 0.07

577 rows × 5 columns

In [32]:
px.bar(df6, x="District",y="Yield", hover_data=["Season","Crop"],color="Season")
In [33]:
px.sunburst(df6,values="Yield",color="Yield",path=["Crop","District"])
In [34]:
df51 = df3.query("Year=='1997-98'")
df61=df51.sort_values("Yield",ascending = False)
df61
Out[34]:
Year District Season Crop Yield
343440 1997-98 DINAJPUR UTTAR Whole Year Coconut 20975.18
343444 1997-98 HOOGHLY Whole Year Coconut 20000.00
343480 1997-98 PURULIA Whole Year Coconut 20000.00
343464 1997-98 MEDINIPUR WEST Whole Year Coconut 19100.00
343436 1997-98 DINAJPUR DAKSHIN Whole Year Coconut 18234.69
... ... ... ... ... ...
344291 1997-98 MURSHIDABAD Summer Moong(Green Gram) 0.02
344221 1997-98 COOCHBEHAR Summer Moong(Green Gram) 0.02
344220 1997-98 COOCHBEHAR Rabi Moong(Green Gram) 0.02
343405 1997-98 DARJEELING Whole Year Cardamom 0.00
345094 1997-98 24 PARAGANAS SOUTH Whole Year Sunflower 0.00

502 rows × 5 columns

In [35]:
px.bar(df61, x="District",y="Yield", hover_data=["Season","Crop"],color="Season")
In [36]:
df7 = df5.query("District=='MEDINIPUR WEST'")
df7.sort_values("Yield",ascending = False)
Out[36]:
Year District Season Crop Yield
297388 2019-20 MEDINIPUR WEST Whole Year Coconut 13847.05
297854 2019-20 MEDINIPUR WEST Whole Year Sugarcane 64.26
297698 2019-20 MEDINIPUR WEST Rabi Potato 22.79
297476 2019-20 MEDINIPUR WEST Kharif Jute 17.99
297556 2019-20 MEDINIPUR WEST Rabi Maize 3.47
297777 2019-20 MEDINIPUR WEST Summer Rice 3.36
297557 2019-20 MEDINIPUR WEST Summer Maize 3.32
297778 2019-20 MEDINIPUR WEST Winter Rice 2.70
297439 2019-20 MEDINIPUR WEST Rabi Groundnut 2.42
297776 2019-20 MEDINIPUR WEST Autumn Rice 2.39
297555 2019-20 MEDINIPUR WEST Autumn Maize 2.37
297440 2019-20 MEDINIPUR WEST Summer Groundnut 2.14
297924 2019-20 MEDINIPUR WEST Rabi Wheat 1.85
297407 2019-20 MEDINIPUR WEST Rabi Gram 1.60
297588 2019-20 MEDINIPUR WEST Rabi Masoor 1.24
297363 2019-20 MEDINIPUR WEST Rabi Arhar/Tur 1.16
297870 2019-20 MEDINIPUR WEST Kharif Sunflower 1.14
297495 2019-20 MEDINIPUR WEST Rabi Khesari 1.10
297724 2019-20 MEDINIPUR WEST Rabi Rapeseed &Mustard 1.01
297675 2019-20 MEDINIPUR WEST Rabi Peas & beans (Pulses) 0.92
297635 2019-20 MEDINIPUR WEST Kharif Moong(Green Gram) 0.80
297902 2019-20 MEDINIPUR WEST Rabi Urad 0.78
297636 2019-20 MEDINIPUR WEST Rabi Moong(Green Gram) 0.75
297457 2019-20 MEDINIPUR WEST Rabi Horse-gram 0.62
297823 2019-20 MEDINIPUR WEST Summer Sesamum 0.54
297901 2019-20 MEDINIPUR WEST Kharif Urad 0.45
297637 2019-20 MEDINIPUR WEST Summer Moong(Green Gram) 0.40
297512 2019-20 MEDINIPUR WEST Rabi Linseed 0.20
In [37]:
px.bar(df7, x="Crop",y="Season", color="Crop",hover_data=["Yield"])
In [38]:
df71 = df51.query("District=='MEDINIPUR WEST'")
df71.sort_values("Yield",ascending = False)
Out[38]:
Year District Season Crop Yield
343464 1997-98 MEDINIPUR WEST Whole Year Coconut 19100.00
345071 1997-98 MEDINIPUR WEST Whole Year Sugarcane 84.46
344502 1997-98 MEDINIPUR WEST Whole Year Potato 26.43
343880 1997-98 MEDINIPUR WEST Kharif Jute 16.05
343394 1997-98 MEDINIPUR WEST Whole Year Barley 10.00
344168 1997-98 MEDINIPUR WEST Kharif Mesta 8.97
344861 1997-98 MEDINIPUR WEST Whole Year Sannhamp 4.00
344771 1997-98 MEDINIPUR WEST Summer Rice 2.49
344772 1997-98 MEDINIPUR WEST Winter Rice 2.19
344770 1997-98 MEDINIPUR WEST Autumn Rice 1.91
345355 1997-98 MEDINIPUR WEST Rabi Wheat 1.83
343940 1997-98 MEDINIPUR WEST Rabi Khesari 1.82
343295 1997-98 MEDINIPUR WEST Whole Year Arecanut 1.23
345162 1997-98 MEDINIPUR WEST Whole Year Turmeric 1.10
343553 1997-98 MEDINIPUR WEST Whole Year Dry chillies 0.91
344116 1997-98 MEDINIPUR WEST Rabi Masoor 0.82
344048 1997-98 MEDINIPUR WEST Whole Year Maize 0.79
344594 1997-98 MEDINIPUR WEST Whole Year Rapeseed &Mustard 0.76
344375 1997-98 MEDINIPUR WEST Rabi Oilseeds total 0.67
344938 1997-98 MEDINIPUR WEST Whole Year Sesamum 0.64
345265 1997-98 MEDINIPUR WEST Kharif Urad 0.64
343665 1997-98 MEDINIPUR WEST Rabi Gram 0.61
343735 1997-98 MEDINIPUR WEST Whole Year Groundnut 0.59
344277 1997-98 MEDINIPUR WEST Kharif Moong(Green Gram) 0.55
343796 1997-98 MEDINIPUR WEST Kharif Horse-gram 0.50
344278 1997-98 MEDINIPUR WEST Rabi Moong(Green Gram) 0.50
345266 1997-98 MEDINIPUR WEST Rabi Urad 0.50
345020 1997-98 MEDINIPUR WEST Kharif Soyabean 0.40
343343 1997-98 MEDINIPUR WEST Whole Year Arhar/Tur 0.20
343993 1997-98 MEDINIPUR WEST Whole Year Linseed 0.11
344354 1997-98 MEDINIPUR WEST Whole Year Niger seed 0.10
344838 1997-98 MEDINIPUR WEST Whole Year Safflower 0.08
343408 1997-98 MEDINIPUR WEST Whole Year Castor seed 0.07
344279 1997-98 MEDINIPUR WEST Summer Moong(Green Gram) 0.04
In [39]:
px.bar(df71, x="Crop",y="Season", color="Crop",hover_data=["Yield"])
In [40]:
data_Assam = agri_data.query("State=='Assam'")
data_Assam
Out[40]:
State District Crop Year Season Area Area Units Production Production Units Yield
3091 Assam BARPETA Arecanut 2001-02 Whole Year 6582.0 Hectare 5326.0 Tonnes 0.809177
3092 Assam BARPETA Arecanut 2002-03 Whole Year 6577.0 Hectare 4671.0 Tonnes 0.710202
3093 Assam BARPETA Arecanut 2003-04 Whole Year 6610.0 Hectare 7728.0 Tonnes 1.169138
3094 Assam BONGAIGAON Arecanut 2001-02 Whole Year 3034.0 Hectare 4024.0 Tonnes 1.326302
3095 Assam BONGAIGAON Arecanut 2002-03 Whole Year 3012.0 Hectare 2785.0 Tonnes 0.924635
... ... ... ... ... ... ... ... ... ... ...
303611 Assam SONITPUR Wheat 2000-01 Rabi 3690.0 Hectare 4085.0 Tonnes 1.107046
303612 Assam TINSUKIA Wheat 1997-98 Rabi 630.0 Hectare 819.0 Tonnes 1.300000
303613 Assam TINSUKIA Wheat 1998-99 Rabi 350.0 Hectare 335.0 Tonnes 0.957143
303614 Assam TINSUKIA Wheat 1999-00 Rabi 276.0 Hectare 352.0 Tonnes 1.275362
303615 Assam TINSUKIA Wheat 2000-01 Rabi 193.0 Hectare 235.0 Tonnes 1.217617

18179 rows × 10 columns

In [41]:
df22 = data_Assam[["Year",'District',"Season","Crop",'Yield']].sort_values("Yield",ascending = False).round(2)
df22
Out[41]:
Year District Season Crop Yield
228210 2018-19 TINSUKIA Whole Year Coconut 43958.33
279641 2019-20 TINSUKIA Whole Year Coconut 32957.75
179341 2015-16 TINSUKIA Whole Year Coconut 30093.33
228208 2016-17 TINSUKIA Whole Year Coconut 28789.47
45525 2005-06 KAMRUP Whole Year Coconut 28334.16
... ... ... ... ... ...
301449 1997-98 KARBI ANGLONG Kharif Castor seed 0.00
303401 1997-98 KAMRUP Whole Year Tobacco 0.00
4433 2003-04 CACHAR Summer Rice 0.00
4505 2003-04 HAILAKANDI Summer Rice 0.00
3927 2003-04 GOALPARA Kharif Mesta 0.00

18179 rows × 5 columns

In [42]:
px.treemap(df22,values="Yield",color="District",path=["Crop","District"])
In [43]:
px.bar(df22.head(10), x="Year",y="Yield", hover_data=["District","Season","Crop"],title="Top 10 Yields of Assam",color="District")
In [44]:
df33 = data_Assam[["Year",'District',"Season","Crop",'Yield']].sort_values("Year",ascending = False).round(2)
df33
Out[44]:
Year District Season Crop Yield
279635 2019-20 MAJULI Whole Year Coconut 10540.74
280058 2019-20 BAKSA Winter Rice 2.04
280065 2019-20 CACHAR Autumn Rice 1.82
279698 2019-20 DIMA HASAO Whole Year Ginger 9.13
280064 2019-20 BONGAIGAON Winter Rice 1.69
... ... ... ... ... ...
302517 1997-98 MARIGAON Whole Year Potato 6.36
303191 1997-98 DARRANG Whole Year Sweet potato 2.50
302144 1997-98 GOALPARA Kharif Mesta 4.86
302140 1997-98 DIMA HASAO Kharif Mesta 4.33
302029 1997-98 CACHAR Kharif Maize 0.51

18179 rows × 5 columns

In [45]:
fig=px.line(df33,x="Year",y="Yield",color="Crop",title="Assam Agriculture")
fig.show()
In [46]:
fig=px.scatter(df33,y = "Yield",x = "Year", hover_data = ["District","Season","Crop"],title = "Crops in Assam",
               color ="District",symbol="Season",size="Yield")
fig.show()
In [47]:
df44=round(pd.pivot_table(data_Assam, index = ["Year"], values=["Yield"], columns=["District"]),2)
df44
Out[47]:
Yield
District BAKSA BARPETA BISWANATH BONGAIGAON CACHAR CHARAIDEO CHIRANG DARRANG DHEMAJI DHUBRI ... MAJULI MARIGAON NAGAON NALBARI SIVASAGAR SONITPUR SOUTH SALMARA MANCACHAR TINSUKIA UDALGURI WEST KARBI ANGLONG
Year
1997-98 NaN 280.88 NaN 102.23 68.81 NaN NaN 409.05 96.90 81.82 ... NaN 447.37 343.22 188.03 234.94 275.67 NaN 66.43 NaN NaN
1998-99 NaN 260.33 NaN 138.41 178.66 NaN NaN 205.63 186.71 234.91 ... NaN 249.94 357.38 220.73 278.72 344.25 NaN 197.99 NaN NaN
1999-00 NaN 255.45 NaN 219.22 78.57 NaN NaN 238.71 48.56 139.96 ... NaN 170.25 434.19 226.08 158.91 299.91 NaN 195.53 NaN NaN
2000-01 NaN 277.42 NaN 208.34 79.14 NaN NaN 192.04 218.87 149.37 ... NaN 204.19 285.57 210.60 241.52 264.31 NaN 56.40 NaN NaN
2001-02 NaN 230.42 NaN 250.34 60.68 NaN NaN 260.01 143.86 51.73 ... NaN 182.31 262.48 213.93 283.98 311.73 NaN 89.66 NaN NaN
2002-03 NaN 166.28 NaN 264.85 43.09 NaN NaN 87.50 158.64 124.88 ... NaN 222.43 330.25 123.18 250.53 343.72 NaN 290.60 NaN NaN
2003-04 NaN 207.18 NaN 269.35 54.93 NaN NaN 235.77 144.39 157.77 ... NaN 228.34 279.31 187.03 220.50 318.29 NaN 291.74 NaN NaN
2004-05 NaN 2.60 NaN 3.05 2.98 NaN NaN 2.81 2.66 2.94 ... NaN 2.63 2.89 2.72 3.19 3.00 NaN 2.89 NaN NaN
2005-06 3.29 138.70 NaN 225.42 58.54 NaN 3.17 256.21 113.88 298.73 ... NaN 196.89 383.73 278.72 261.51 367.67 NaN 135.78 3.26 NaN
2006-07 241.78 226.36 NaN 220.58 68.72 NaN 241.64 79.04 69.36 261.16 ... NaN 243.58 348.54 162.66 247.91 115.87 NaN 48.39 188.69 NaN
2007-08 231.58 231.52 NaN 195.94 33.14 NaN 231.61 233.67 65.28 367.56 ... NaN 199.42 256.64 159.11 338.49 256.02 NaN 47.46 221.17 NaN
2008-09 287.57 290.77 NaN 215.21 97.64 NaN 287.49 184.16 104.92 355.97 ... NaN 220.65 329.16 235.03 289.03 311.56 NaN 33.70 253.66 NaN
2009-10 281.40 270.90 NaN 206.56 291.56 NaN 281.39 232.85 329.03 469.25 ... NaN 176.13 357.72 178.92 352.43 390.29 NaN 261.75 138.07 NaN
2010-11 249.83 246.86 NaN 248.42 98.39 NaN 257.71 184.90 77.23 391.24 ... NaN 206.68 297.18 178.36 323.20 408.62 NaN 140.05 161.78 NaN
2011-12 146.21 495.18 NaN 257.86 89.31 NaN 255.07 239.99 159.36 497.52 ... NaN 297.10 218.19 174.01 172.19 401.58 NaN 146.00 95.41 NaN
2012-13 190.09 134.57 NaN 225.84 68.84 NaN 190.33 264.81 135.32 277.63 ... NaN 302.59 195.15 230.29 199.00 260.05 NaN 142.17 132.48 NaN
2013-14 213.52 396.25 NaN 321.86 38.05 NaN 214.04 230.04 45.52 343.13 ... NaN 210.89 140.20 222.15 112.62 227.65 NaN 420.18 235.48 NaN
2014-15 208.48 292.77 NaN 221.75 98.85 NaN 215.25 213.48 43.90 485.85 ... NaN 232.97 192.88 291.86 189.45 178.41 NaN 468.67 63.02 NaN
2015-16 229.85 320.87 NaN 178.26 38.38 NaN 229.73 220.68 46.94 390.57 ... NaN 225.94 153.12 397.23 50.57 237.93 NaN 944.18 209.26 NaN
2016-17 212.51 417.60 NaN 205.79 85.96 NaN 212.66 172.67 120.73 476.37 ... NaN 172.35 123.02 408.68 130.18 201.31 NaN 932.45 105.08 NaN
2017-18 212.02 544.83 NaN 263.94 103.95 NaN 475.95 136.01 131.26 549.01 ... NaN 216.94 133.14 310.21 160.07 177.41 NaN 686.31 141.14 NaN
2018-19 215.75 447.12 NaN 262.17 58.71 NaN 78.19 167.14 51.43 533.34 ... NaN 203.46 137.29 319.44 121.49 199.36 NaN 1335.70 103.46 NaN
2019-20 212.23 490.11 5628.9 235.27 50.27 2636.34 61.87 153.29 112.51 397.62 ... 5270.6 200.42 119.90 351.52 189.04 127.20 0.48 1102.93 100.78 2026.96

23 rows × 33 columns

In [48]:
max(df44)
Out[48]:
('Yield', 'WEST KARBI ANGLONG')
In [49]:
df44.describe().round(2)
Out[49]:
Yield
District BAKSA BARPETA BISWANATH BONGAIGAON CACHAR CHARAIDEO CHIRANG DARRANG DHEMAJI DHUBRI ... MAJULI MARIGAON NAGAON NALBARI SIVASAGAR SONITPUR SOUTH SALMARA MANCACHAR TINSUKIA UDALGURI WEST KARBI ANGLONG
count 15.00 23.00 1.0 23.00 23.00 1.00 15.00 23.00 23.00 23.00 ... 1.0 23.00 23.00 23.00 23.00 23.00 1.00 23.00 15.00 1.00
mean 209.07 288.04 5628.9 214.81 80.31 2636.34 215.74 200.02 113.36 306.01 ... 5270.6 217.98 247.01 229.15 209.11 261.82 0.48 349.43 143.52 2026.96
std 66.45 129.26 NaN 64.61 57.35 NaN 110.21 79.09 70.70 163.41 ... NaN 74.95 108.63 90.64 88.65 100.44 NaN 383.57 69.10 NaN
min 3.29 2.60 5628.9 3.05 2.98 2636.34 3.17 2.81 2.66 2.94 ... 5270.6 2.63 2.89 2.72 3.19 3.00 0.48 2.89 3.26 2026.96
25% 210.25 228.39 5628.9 206.18 52.60 2636.34 201.50 169.90 58.36 153.57 ... 5270.6 198.15 146.66 178.64 159.49 200.34 0.48 78.04 102.12 2026.96
50% 213.52 270.90 5628.9 221.75 68.81 2636.34 229.73 213.48 112.51 343.13 ... 5270.6 210.89 262.48 220.73 220.50 264.31 0.48 195.53 138.07 2026.96
75% 236.68 358.56 5628.9 254.10 93.48 2636.34 256.39 237.24 144.12 433.44 ... 5270.6 230.66 336.74 285.29 270.12 331.00 0.48 444.42 198.98 2026.96
max 287.57 544.83 5628.9 321.86 291.56 2636.34 475.95 409.05 329.03 549.01 ... 5270.6 447.37 434.19 408.68 352.43 408.62 0.48 1335.70 253.66 2026.96

8 rows × 33 columns

In [50]:
fig=px.box(df33,y="District",x="Year")
fig.show()
In [51]:
df55 = df33.query("Year=='2019-20'")
df66=df55.sort_values("Yield",ascending = False)
df66
Out[51]:
Year District Season Crop Yield
279641 2019-20 TINSUKIA Whole Year Coconut 32957.75
279613 2019-20 BARPETA Whole Year Coconut 16018.95
279630 2019-20 KAMRUP METRO Whole Year Coconut 14340.91
279628 2019-20 JORHAT Whole Year Coconut 13865.03
279629 2019-20 KAMRUP Whole Year Coconut 13004.18
... ... ... ... ... ...
279486 2019-20 DIBRUGARH Rabi Arecanut 0.30
279498 2019-20 LAKHIMPUR Rabi Arecanut 0.26
280142 2019-20 DHEMAJI Kharif Sesamum 0.25
280032 2019-20 CACHAR Rabi Rapeseed &Mustard 0.22
280267 2019-20 BAKSA Whole Year Tobacco 0.14

880 rows × 5 columns

In [52]:
px.bar(df66, x="District",y="Yield", hover_data=["Season","Crop"],color="Season")
In [53]:
px.sunburst(df66,values="Yield",color="Yield",path=["Crop","District"])
In [54]:
df77 = df55.query("District=='KAMRUP'")
df77.sort_values("Yield",ascending = False)
Out[54]:
Year District Season Crop Yield
279629 2019-20 KAMRUP Whole Year Coconut 13004.18
280202 2019-20 KAMRUP Whole Year Sugarcane 29.72
279550 2019-20 KAMRUP Whole Year Banana 21.04
279935 2019-20 KAMRUP Whole Year Onion 14.40
280016 2019-20 KAMRUP Whole Year Potato 9.81
279703 2019-20 KAMRUP Whole Year Ginger 8.92
280256 2019-20 KAMRUP Whole Year Tapioca 7.63
279805 2019-20 KAMRUP Kharif Maize 5.97
279858 2019-20 KAMRUP Kharif Mesta 5.30
279755 2019-20 KAMRUP Kharif Jute 5.27
280229 2019-20 KAMRUP Whole Year Sweet potato 4.63
280099 2019-20 KAMRUP Winter Rice 2.61
280098 2019-20 KAMRUP Summer Rice 2.59
279577 2019-20 KAMRUP Whole Year Black pepper 1.82
280292 2019-20 KAMRUP Whole Year Turmeric 1.20
279989 2019-20 KAMRUP Rabi Peas & beans (Pulses) 1.05
279523 2019-20 KAMRUP Kharif Arhar/Tur 0.92
279676 2019-20 KAMRUP Whole Year Dry chillies 0.90
280097 2019-20 KAMRUP Autumn Rice 0.88
280345 2019-20 KAMRUP Rabi Wheat 0.84
280319 2019-20 KAMRUP Rabi Urad 0.66
279962 2019-20 KAMRUP Rabi Other Rabi pulses 0.64
279493 2019-20 KAMRUP Rabi Arecanut 0.63
280150 2019-20 KAMRUP Kharif Sesamum 0.63
280043 2019-20 KAMRUP Rabi Rapeseed &Mustard 0.60
279779 2019-20 KAMRUP Rabi Linseed 0.54
280176 2019-20 KAMRUP Kharif Small millets 0.54
279832 2019-20 KAMRUP Rabi Masoor 0.53
279910 2019-20 KAMRUP Kharif Niger seed 0.52
279602 2019-20 KAMRUP Kharif Castor seed 0.50
279884 2019-20 KAMRUP Rabi Moong(Green Gram) 0.46
279729 2019-20 KAMRUP Rabi Gram 0.32
In [55]:
px.bar(df77, x="Crop",y="Season", color="Crop",hover_data=["Yield"])
In [56]:
df551 = df33.query("Year=='1997-98'")
df661=df551.sort_values("Yield",ascending = False)
df661
Out[56]:
Year District Season Crop Yield
301545 1997-98 LAKHIMPUR Whole Year Coconut 13454.55
301549 1997-98 MARIGAON Whole Year Coconut 11990.98
301493 1997-98 DARRANG Whole Year Coconut 10145.83
301553 1997-98 NAGAON Whole Year Coconut 8492.03
301481 1997-98 BARPETA Whole Year Coconut 7213.01
... ... ... ... ... ...
301460 1997-98 MARIGAON Kharif Castor seed 0.20
303417 1997-98 MARIGAON Whole Year Tobacco 0.20
301456 1997-98 LAKHIMPUR Kharif Castor seed 0.00
301449 1997-98 KARBI ANGLONG Kharif Castor seed 0.00
303401 1997-98 KAMRUP Whole Year Tobacco 0.00

568 rows × 5 columns

In [57]:
px.bar(df661, x="District",y="Yield", hover_data=["Season","Crop"],color="Season")
In [58]:
df771 = df551.query("District=='KAMRUP'")
df771.sort_values("Yield",ascending = False)
Out[58]:
Year District Season Crop Yield
301529 1997-98 KAMRUP Whole Year Coconut 6908.90
303135 1997-98 KAMRUP Kharif Sugarcane 74.99
302497 1997-98 KAMRUP Whole Year Potato 10.52
301890 1997-98 KAMRUP Kharif Jute 7.60
302151 1997-98 KAMRUP Kharif Mesta 4.81
303319 1997-98 KAMRUP Whole Year Tapioca 3.60
302313 1997-98 KAMRUP Whole Year Onion 2.75
303227 1997-98 KAMRUP Whole Year Sweet potato 2.49
302778 1997-98 KAMRUP Summer Rice 1.34
303576 1997-98 KAMRUP Rabi Wheat 1.18
302779 1997-98 KAMRUP Winter Rice 1.03
303485 1997-98 KAMRUP Whole Year Turmeric 0.87
301299 1997-98 KAMRUP Kharif Arhar/Tur 0.80
302777 1997-98 KAMRUP Autumn Rice 0.73
302069 1997-98 KAMRUP Kharif Maize 0.62
301207 1997-98 KAMRUP Whole Year Arecanut 0.61
302589 1997-98 KAMRUP Rabi Rapeseed &Mustard 0.51
302227 1997-98 KAMRUP Whole Year Niger seed 0.50
302405 1997-98 KAMRUP Rabi Other Rabi pulses 0.49
301621 1997-98 KAMRUP Kharif Cotton(lint) 0.48
301712 1997-98 KAMRUP Whole Year Dry chillies 0.45
301978 1997-98 KAMRUP Rabi Linseed 0.44
302957 1997-98 KAMRUP Whole Year Sesamum 0.41
301803 1997-98 KAMRUP Rabi Gram 0.31
303401 1997-98 KAMRUP Whole Year Tobacco 0.00
In [59]:
px.bar(df771, x="Crop",y="Season", color="Crop",hover_data=["Yield"])

3. Sustainable agricultural practices:¶

   1) Rotating crops and embracing diversity
   2) Adopting agroforestry practices
   3) Managing whole systems and landscapes
   4) Polyculture Farming
   5) Improve Soil Fertility and Soil Management
   6) Save Transportation Costs
   7) Better Water Management
   8) Urban Agriculture
   9) Improving the ecological conditions